Uromodulin in sepsis and severe pneumonia: a two-sample Mendelian randomization study.

The outcome for patients with sepsis-associated acute kidney injury in the intensive care unit (ICU) remains poor. Low serum uromodulin (sUMOD) protein levels have been proposed as a causal mediator of this effect. We investigated the effect of different levels of sUMOD on the risk of sepsis and severe pneumonia and outcomes in these conditions. A two-sample Mendelian randomization (MR) study was performed. Single-nucleotide polymorphisms (SNPs) associated with increased levels of sUMOD were identified and used as instrumental variables for association with outcomes. Data from different cohorts were combined based on disease severity and meta-analyzed. Five SNPs associated with increased sUMOD levels were identified and tested in six datasets from two biobanks. There was no protective effect of increased levels of sUMOD on the risk of sepsis [two cohorts, odds ratio (OR) 0.99 (95% confidence interval 0.95-1.03), P = 0.698, and OR 0.95 (0.91-1.00), P = 0.060, respectively], risk of sepsis requiring ICU admission [OR 1.04 (0.93-1.16), P = 0.467], ICU mortality in sepsis [OR 1.00 (0.74-1.37), P = 0.987], risk of pneumonia requiring ICU admission [OR 1.05 (0.98-1.14), P = 0.181], or ICU mortality in pneumonia [OR 1.17 (0.98-1.39), P = 0.079]. Meta-analysis of hospital-admitted and ICU-admitted patients separately yielded similar results [OR 0.98 (0.95-1.01), P = 0.23, and OR 1.05 (0.99-1.12), P = 0.86, respectively]. Among patients with sepsis and severe pneumonia, there was no protective effect of different levels of sUMOD. Results were consistent regardless of geographic origins and not modified by disease severity. NEW & NOTEWORTHY The presence of acute kidney injury in severe infections increases the likelihood of poor outcome severalfold. A decrease in serum uromodulin (sUMOD), synthetized in the kidney, has been proposed as a mediator of this effect. Using the Mendelian randomization technique, we tested the hypothesis that increased sUMOD is protective in severe infections. Analyses, however, showed no evidence of a protective effect of higher levels of sUMOD in sepsis or severe pneumonia.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

The Effect of Renal Replacement Therapy and Antibiotic Dose on Antibiotic Concentrations in Critically Ill Patients: Data From the Multinational Sampling Antibiotics in Renal Replacement Therapy Study.

BACKGROUND: The optimal dosing of antibiotics in critically ill patients receiving renal replacement therapy (RRT) remains unclear. In this study, we describe the variability in RRT techniques and antibiotic dosing in critically ill patients receiving RRT and relate observed trough antibiotic concentrations to optimal targets. METHODS: We performed a prospective, observational, multinational, pharmacokinetic study in 29 intensive care units from 14 countries. We collected demographic, clinical, and RRT data. We measured trough antibiotic concentrations of meropenem, piperacillin-tazobactam, and vancomycin and related them to high- and low-target trough concentrations. RESULTS: We studied 381 patients and obtained 508 trough antibiotic concentrations. There was wide variability (4-8-fold) in antibiotic dosing regimens, RRT prescription, and estimated endogenous renal function. The overall median estimated total renal clearance (eTRCL) was 50 mL/minute (interquartile range [IQR], 35-65) and higher eTRCL was associated with lower trough concentrations for all antibiotics (P < .05). The median (IQR) trough concentration for meropenem was 12.1 mg/L (7.9-18.8), piperacillin was 78.6 mg/L (49.5-127.3), tazobactam was 9.5 mg/L (6.3-14.2), and vancomycin was 14.3 mg/L (11.6-21.8). Trough concentrations failed to meet optimal higher limits in 26%, 36%, and 72% and optimal lower limits in 4%, 4%, and 55% of patients for meropenem, piperacillin, and vancomycin, respectively. CONCLUSIONS: In critically ill patients treated with RRT, antibiotic dosing regimens, RRT prescription, and eTRCL varied markedly and resulted in highly variable antibiotic concentrations that failed to meet therapeutic targets in many patients.

Identifying 'at-risk' critically ill patients who present to the emergency department and require intensive care unit admission: A retrospective observational cohort study.

BACKGROUND: Emergency department (ED) triage is the process of prioritising patients by medical urgency. Delays in intensive care unit (ICU) admission can adversely affect patients. OBJECTIVES: This study aimed to identify characteristics associated with ICU admission for patients triaged as Australasian Triage Scale (ATS) 3 but subsequently admitted to the ICU within 24 h of triage. METHODS: This retrospective, observational cohort study was conducted in a public teaching hospital in Queensland, Australia. Patients older than 18 y triaged with an ATS 3 and admitted to the ICU within 24 h of triage or admitted to the ward between January 1, 2012, and December 31, 2012, were included. The demographic and clinical profiles of ICU admissions vs. all other ward admissions for patients triaged an ATS of 3 were compared. Multivariable regression analysis compared characteristics of patients triaged with an ATS of 3 who did and did not require ICU transfer. Descriptive data are reported as n (%) and median and interquartile range (IQR). Regression analysis is reported as adjusted odds ratios (aORs) with 95% confidence intervals (95% CIs). RESULTS: Of the 27 454 adult ED presentations triaged with an ATS of 3, 22.4% (n = 6138) required hospital admission, comprising 5302 individuals, 2.1% of whom (n = 110) were admitted to the ICU within 24 h of triage. Age- and sex-adjusted predictors of ICU admission for patients triaged with an ATS of 3 included infectious (aOR: 3.7; 95% CI: 2.0-6.9), neurological (aOR: 2.8; 95% CI: 1.6-5.0), and gastrointestinal disorders (aOR: 2.2; 95% CI 1.2-3.5); arriving by ambulance; arriving after hours; or arriving on weekends. Regardless of diagnosis or sex, persons older than 80 y were less likely to be admitted to the ICU (aOR: 0.4; 95% CI: 0.2-0.8). CONCLUSIONS: Patients triaged as ATS 3 presenting on weekends or after hours, and those with infectious, gastrointestinal, or neurological conditions warrant careful attention as these factors were associated with higher odds of ICU admission. Ongoing staff education regarding triage and signs of deterioration are important to prevent avoidable outcomes.

Utilizing heart rate variability to predict ICU patient outcome in traumatic brain injury.

BACKGROUND: Prediction of patient outcome in medical intensive care units (ICU) may help for development and investigation of early interventional strategies. Several ICU scoring systems have been developed and are used to predict clinical outcome of ICU patients. These scores are calculated from clinical physiological and biochemical characteristics of patients. Heart rate variability (HRV) is a correlate of cardiac autonomic regulation and has been evident as a marker of poor clinical prognosis. HRV can be measured from the electrocardiogram non-invasively and monitored in real time. HRV has been identified as a promising 'electronic biomarker' of disease severity. Traumatic brain injury (TBI) is a subset of critically ill patients admitted to ICU, with significant morbidity and mortality, and often difficult to predict outcomes. Changes of HRV for brain injured patients have been reported in several studies. This study aimed to utilize the continuous HRV collection from admission across the first 24 h in the ICU in severe TBI patients to develop a patient outcome prediction system. RESULTS: A feature extraction strategy was applied to measure the HRV fluctuation during time. A prediction model was developed based on HRV measures with a genetic algorithm for feature selection. The result (AUC: 0.77) was compared with earlier reported scoring systems (highest AUC: 0.76), encouraging further development and practical application. CONCLUSIONS: The prediction models built with different feature sets indicated that HRV based parameters may help predict brain injury patient outcome better than the previously adopted illness severity scores.

Investigating healthcare contacts of Dialysis patients by age and gender.

BACKGROUND: The objective of this paper is to utilise a clinical costing system to investigate differences in the patient journey, defined as the sequence and timing of contacts with the Gold Coast Hospital and Health Services (GCHHS), for four dialysis patient groups defined based on age and gender. It is hypothesised that frequency of contact and form of contact will differ based on both gender and age. METHODS: Data were provided for 393 patients discharged from the GCHHS facility with dialysis treatment between the 1st of January 2015 and the 31st of December 2016. Features extracted from the data included the number and type of contacts (inpatient admissions, outpatient appointments, and emergency department presentations), the likelihood of subsequent contact types, and time spent in and between contact types. Likelihoods of subsequent contact types were estimated by treating the sequence of contacts observed for each patient as a Markov chain and estimating transition probabilities. RESULTS: Differences in patient journey were most prominent when considering age differences, with older patients being characterised by a greater volume of average contacts over the two-year period. The larger volume of average contacts was attributable to shorter times between all types of contacts with the GCHHS as well as an increased volume of inpatient admissions for older patients. Patient journeys did not consistently differ by gender, though some isolated differences were noted for older female patients relative to older male patients. CONCLUSIONS: Different patient groups are characterised by different patient journeys, and better understanding these differences will facilitate improved management of the resources required to service these patients. Clinical costing systems represent a valuable and easily accessible source of data for formulating institution-specific expectations of healthcare utilisation for different groups.

Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.

BACKGROUND: Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admission, we aimed to develop more accurate models of risk prediction using both logistic regression (LR) and machine learning (ML) techniques, with a combination of demographic, physiologic, and biochemical information. METHODS AND FINDINGS: Patient-level data were extracted from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for patients who had experienced a cardiac arrest within 24 hours prior to admission to an intensive care unit (ICU) during the period January 2006 to December 2016. The primary outcome was in-hospital mortality. The models were trained and tested on a dataset (split 90:10) including age, lowest and highest physiologic variables during the first 24 hours, and key past medical history. LR and 5 ML approaches (gradient boosting machine [GBM], support vector classifier [SVC], random forest [RF], artificial neural network [ANN], and an ensemble) were compared to the APACHE III and Australian and New Zealand Risk of Death (ANZROD) predictions. In all, 39,566 patients from 186 ICUs were analysed. Mean (±SD) age was 61 ± 17 years; 65% were male. Overall in-hospital mortality was 45.5%. Models were evaluated in the test set. The APACHE III and ANZROD scores demonstrated good discrimination (area under the receiver operating characteristic curve [AUROC] = 0.80 [95% CI 0.79-0.82] and 0.81 [95% CI 0.8-0.82], respectively) and modest calibration (Brier score 0.19 for both), which was slightly improved by LR (AUROC = 0.82 [95% CI 0.81-0.83], DeLong test, p < 0.001). Discrimination was significantly improved using ML models (ensemble and GBM AUROCs = 0.87 [95% CI 0.86-0.88], DeLong test, p < 0.001), with an improvement in performance (Brier score reduction of 22%). Explainability models were created to assist in identifying the physiologic features that most contributed to an individual patient's survival. Key limitations include the absence of pre-hospital data and absence of external validation. CONCLUSIONS: ML approaches significantly enhance predictive discrimination for mortality following cardiac arrest compared to existing illness severity scores and LR, without the use of pre-hospital data. The discriminative ability of these ML models requires validation in external cohorts to establish generalisability.

A rare variant association test in family-based designs and non-normal quantitative traits.

Rare variant studies are now being used to characterize the genetic diversity between individuals and may help to identify substantial amounts of the genetic variation of complex diseases and quantitative phenotypes. Family data have been shown to be powerful to interrogate rare variants. Consequently, several rare variants association tests have been recently developed for family-based designs, but typically, these assume the normality of the quantitative phenotypes. In this paper, we present a family-based test for rare-variants association in the presence of non-normal quantitative phenotypes. The proposed model relaxes the normality assumption and does not specify any parametric distribution for the marginal distribution of the phenotype. The dependence between relatives is modeled via a Gaussian copula. A score-type test is derived, and several strategies to approximate its distribution under the null hypothesis are derived and investigated. The performance of the proposed test is assessed and compared with existing methods by simulations. The methodology is illustrated with an association study involving the adiponectin trait from the UK10K project.

Identifying therapeutic targets for cancer among 2074 circulating proteins and risk of nine cancers.

Circulating proteins can reveal key pathways to cancer and identify therapeutic targets for cancer prevention. We investigate 2,074 circulating proteins and risk of nine common cancers (bladder, breast, endometrium, head and neck, lung, ovary, pancreas, kidney, and malignant non-melanoma) using cis protein Mendelian randomisation and colocalization. We conduct additional analyses to identify adverse side-effects of altering risk proteins and map cancer risk proteins to drug targets. Here we find 40 proteins associated with common cancers, such as PLAUR and risk of breast cancer [odds ratio per standard deviation increment: 2.27, 1.88-2.74], and with high-mortality cancers, such as CTRB1 and pancreatic cancer [0.79, 0.73-0.85]. We also identify potential adverse effects of protein-altering interventions to reduce cancer risk, such as hypertension. Additionally, we report 18 proteins associated with cancer risk that map to existing drugs and 15 that are not currently under clinical investigation. In sum, we identify protein-cancer links that improve our understanding of cancer aetiology. We also demonstrate that the wider consequence of any protein-altering intervention on well-being and morbidity is required to interpret any utility of proteins as potential future targets for therapeutic prevention.

Making sense of intratumor genetic heterogeneity: altered frequency of androgen receptor CAG repeat length variants in breast cancer tissues.

To examine the significance of intratumor genetic heterogeneity (ITGH) of the androgen receptor (AR) gene in breast cancer, patient-matched samples of laser capture microdissected breast tumor cells, adjacent normal breast epithelia cells, and peripheral blood leukocytes were sequenced using a novel next generation sequencing protocol. This protocol measured the frequency of distribution of a variable AR CAG repeat length, a functional polymorphism associated with breast cancer risk. All samples exhibited some degree of ITGH with up to 30 CAG repeat length variants identified. Each type of tissue exhibited a different distribution profile of CAG repeat lengths with substantial differences in the frequencies of zero and 18-25 CAG AR variants. Tissue differences in the frequency of ARs with each of these CAG repeat lengths were significant as measured by paired, twin t-tests. These results suggest that preferential selection of 18-25 CAG repeat length variants in breast tumors may be associated with breast cancer, and support the observation that shorter CAG repeats may protect against breast cancer. They also suggest that merely identifying variant genes will be insufficient to determine the critical mutational events of oncogenesis, which will require measuring the frequency of distribution of mutations within cancerous and matching normal tissues.

Genetic regulation of vitamin D levels.

Vitamin D plays several roles in the body, influencing bone health as well as serum calcium and phosphate levels. Further, vitamin D may modify immune function, cell proliferation, differentiation, and apoptosis. Vitamin D deficiency has been associated with numerous health outcomes, including bone disease, cancer, autoimmune disease, infectious disease, type 1 and type 2 diabetes, hypertension, and heart disease, although it is unclear whether or not these associations are causal. Various twin and family studies have demonstrated moderate to high heritability for circulating vitamin D levels. Accordingly, many studies have investigated the genetic determinants of this hormone. Recent advances in the methodology of large-scale genetic association studies, including coordinated international collaboration, have identified associations of CG, DHCR1, CYP2R1, VDR, and CYP24A1 with serum levels of vitamin D. Here, we review the genetic determinants of vitamin D levels by focusing on new findings arising from candidate gene and genomewide association studies.

Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection.

BACKGROUND: Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. METHODS: Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261). RESULTS: We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. CONCLUSIONS: Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Acute kidney injury (AKI) is a sudden, sometimes fatal, episode of kidney failure or damage. It is a known complication of COVID-19, albeit through unclear mechanisms. COVID-19 is also associated with kidney dysfunction in the long term, or chronic kidney disease (CKD). There is a need to better understand which patients with COVID-19 are at risk of AKI or CKD. We measure levels of several thousand proteins in the blood of hospitalized COVID-19 patients. We discover and validate sets of proteins associated with severe AKI and CKD in these patients. The markers identified suggest that kidney injury in COVID-19 patients involves damage to kidney cells that reabsorb fluid from urine and reduced blood flow to the heart, causing damage to heart muscles. Our findings might help clinicians to predict kidney injury in patients with COVID-19, and to understand its mechanisms.

Critical care services and 2009 H1N1 influenza in Australia and New Zealand.

BACKGROUND: Planning for the treatment of infection with the 2009 pandemic influenza A (H1N1) virus through health care systems in developed countries during winter in the Northern Hemisphere is hampered by a lack of information from similar health care systems. METHODS: We conducted an inception-cohort study in all Australian and New Zealand intensive care units (ICUs) during the winter of 2009 in the Southern Hemisphere. We calculated, per million inhabitants, the numbers of ICU admissions, bed-days, and days of mechanical ventilation due to infection with the 2009 H1N1 virus. We collected data on demographic and clinical characteristics of the patients and on treatments and outcomes. RESULTS: From June 1 through August 31, 2009, a total of 722 patients with confirmed infection with the 2009 H1N1 virus (28.7 cases per million inhabitants; 95% confidence interval [CI], 26.5 to 30.8) were admitted to an ICU in Australia or New Zealand. Of the 722 patients, 669 (92.7%) were under 65 years of age and 66 (9.1%) were pregnant women; of the 601 adults for whom data were available, 172 (28.6%) had a body-mass index (the weight in kilograms divided by the square of the height in meters) greater than 35. Patients infected with the 2009 H1N1 virus were in the ICU for a total of 8815 bed-days (350 per million inhabitants). The median duration of treatment in the ICU was 7.0 days (interquartile range, 2.7 to 13.4); 456 of 706 patients (64.6%) with available data underwent mechanical ventilation for a median of 8 days (interquartile range, 4 to 16). The maximum daily occupancy of the ICU was 7.4 beds (95% CI, 6.3 to 8.5) per million inhabitants. As of September 7, 2009, a total of 103 of the 722 patients (14.3%; 95% CI, 11.7 to 16.9) had died, and 114 (15.8%) remained in the hospital. CONCLUSIONS: The 2009 H1N1 virus had a substantial effect on ICUs during the winter in Australia and New Zealand. Our data can assist planning for the treatment of patients during the winter in the Northern Hemisphere.

Redesigning the ICU nursing discharge process: a quality improvement study.

PURPOSE: To evaluate the impact of a redesigned intensive care unit (ICU) nursing discharge process on ICU discharge delay, hospital mortality, and ICU readmission within 72 hours. METHODS: A quality improvement study using a time series design and statistical process control analysis was conducted in one Australian general ICU. The primary outcome measure was hours of discharge delay per patient discharged alive per month, measured for 15 months prior to, and for 12 months after the redesigned process was implemented. The redesign process included appointing a change agent to facilitate process improvement, developing a patient handover sheet, requesting ward staff to nominate an estimated transfer time, and designing a daily ICU discharge alert sheet that included an expected date of discharge. RESULTS: A total of 1,787 ICU discharges were included in this study, 1,001 in the 15 months before and 786 in the 12 months after the implementation of the new discharge processes. There was no difference in in-hospital mortality after discharge from ICU or ICU readmission within 72 hours during the study period. However, process improvement was demonstrated by a reduction in the average patient discharge delay time of 3.2 hours (from 4.6 hour baseline to 1.0 hours post-intervention). CONCLUSIONS: Involving both ward and ICU staff in the redesign process may have contributed to a shared situational awareness of the problems, which led to more timely and effective ICU discharge processes. The use of a change agent, whose ongoing role involved follow-up of patients discharged from ICU, may have helped to embed the new process into practice.

An Artificial Intelligence Computer-vision Algorithm to Triage Otoscopic Images From Australian Aboriginal and Torres Strait Islander Children.

OBJECTIVE: To develop an artificial intelligence image classification algorithm to triage otoscopic images from rural and remote Australian Aboriginal and Torres Strait Islander children. STUDY DESIGN: Retrospective observational study. SETTING: Tertiary referral center. PATIENTS: Rural and remote Aboriginal and Torres Strait Islander children who underwent tele-otology ear health screening in the Northern Territory, Australia between 2010 and 2018. INTERVENTIONS: Otoscopic images were labeled by otolaryngologists to classify the ground truth. Deep and transfer learning methods were used to develop an image classification algorithm. MAIN OUTCOME MEASURES: Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, area under the curve (AUC) of the resultant algorithm compared with the ground truth. RESULTS: Six thousand five hundred twenty seven images were used (5927 images for training and 600 for testing). The algorithm achieved an accuracy of 99.3% for acute otitis media, 96.3% for chronic otitis media, 77.8% for otitis media with effusion (OME), and 98.2% to classify wax/obstructed canal. To differentiate between multiple diagnoses, the algorithm achieved 74.4 to 92.8% accuracy and an AUC of 0.963 to 0.997. The most common incorrect classification pattern was OME misclassified as normal tympanic membranes. CONCLUSIONS: The paucity of access to tertiary otolaryngology care for rural and remote Aboriginal and Torres Strait Islander communities may contribute to an under-identification of ear disease. Computer vision image classification algorithms can accurately classify ear disease from otoscopic images of Indigenous Australian children. In the future, a validated algorithm may integrate with existing telemedicine initiatives to support effective triage and facilitate early treatment and referral.

Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection.

Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Using measurements of ∼4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Genetic polymorphisms of innate immunity-related inflammatory pathways and their association with factors related to type 2 diabetes.

BACKGROUND: Type 2 diabetes mellitus (T2DM) has been linked to a state of pre-clinical chronic inflammation resulting from abnormalities in the innate immune pathway. Serum levels of pro-inflammatory cytokines and acute-phase proteins, collectively known as 'inflammatory network', are elevated in the pre-, or early, stages of T2DM and increase with disease progression. Genetic variation can affect the innate immune response to certain environmental factors, and may, therefore, determine an individual's lifetime risk of disease. METHODS: We conducted a cross-sectional study in 6,720 subjects from the Twins UK Registry to evaluate the association between 18 single nucleotide polymorphisms (SNPs) in five genes (TLR4, IL1A, IL6, TNFA, and CRP) along the innate immunity-related inflammatory pathway and biomarkers of predisposition to T2DM [fasting insulin and glucose, HDL- and LDL- cholesterols, triglycerides (TGs), amyloid-A, sensitive C-reactive protein (sCRP) and vitamin D binding protein (VDBP) and body mass index (BMI)]. RESULTS: Of 18 the SNPs examined for their association with nine metabolic phenotypes of interest, six were significantly associated with five metabolic phenotypes (Bonferroni correction, P ≤ 0.0027). Fasting insulin was associated with SNPs in IL6 and TNFA, serum HDL-C with variants of TNFA and CRP and serum sCRP level with SNPs in CRP. Cross-correlation analysis among the different metabolic factors related to risk of T2DM showed several significant associations. For example, BMI was directly correlated with glucose (r = 0.11), insulin (r = 0.15), sCRP (r = 0.23), LDL-C (r = 0.067) and TGs (r = 0.18) but inversely with HDL-C (r = -0.14). sCRP was also positively correlated (P < 0.0001) with insulin (r = 0.17), amyloid-A (r = 0.39), TGs (r = 0.26), and VDBP (r = 0.36) but inversely with HDL-C (r = -0.12). CONCLUSION: Genetic variants in the innate immunity pathway and its related inflammatory cascade is associated with some metabolic risk factors for T2DM; an observation that may provide a rationale for further studying their role as biomarkers for disease early risk prediction.

Mortality rate comparison after switching from continuous to prolonged intermittent renal replacement for acute kidney injury in three intensive care units from different countries.

BACKGROUND: Prolonged intermittent renal replacement therapy (PIRRT) is a dialysis modality for critically ill patients that in theory combines the superior detoxification and haemodynamic stability of the continuous renal replacement therapy (CRRT) with the operational convenience, reduced haemorrhagic risk and low cost of conventional intermittent haemodialysis. However, the extent to which PIRRT should replace these other modalities is uncertain because comparative studies of mortality are lacking. We retrospectively examined the mortality data from three general intensive care units (ICUs) in different countries that have switched their predominant therapeutic approach from CRRT to PIRRT. We assessed whether this practice change was associated with a change in mortality rate. METHODS: Data were analysed from ICUs in New Zealand, Australia and Italy. The study population comprised all patients requiring renal replacement therapy from 1 January 1995 to 31 December 2005 (n = 1347), the period of time spanning the change from CRRT to PIRRT in each unit. Poisson regression models were used to estimate the incident rate ratio (IRR) for death, comparing the periods before and after change to PIRRT in each unit. Estimates were adjusted for patient illness severity (APACHE II score) and for the underlying time trend in mortality rate over time. RESULTS: The change from CRRT to PIRRT was not associated with any increase in mortality rate, with an adjusted IRR of 1.02 (0.61-1.71). The IRR was virtually identical in the three ICUs (P-value = 0.63 for the difference in the IRR between ICUs). CONCLUSIONS: Switching from CRRT to PIRRT was not associated with a change in mortality rate. Pending the results of a randomized trial, our study provides evidence that PIRRT might be equivalent to CRRT in the general ICU patient.

RR-APET - Heart rate variability analysis software.

BACKGROUND AND OBJECTIVES: Heart rate variability (HRV) has increasingly been linked to medical phenomena and several HRV metrics have been found to be good indicators of patient health. This has enabled generalised treatment plans to be developed in order to respond to subtle personal differences that are reflected in HRV metrics. There are several established HRV analysis platforms and methods available within the literature; some of which provide command line operation across databases but do not offer extensive graphical user interface (GUI) and editing functionality, while others offer extensive ECG editing but are not feasible over large datasets without considerable manual effort. The aim of this work is to provide a comprehensive open-source package, in a well known and multi-platform language, that offers considerable graphical signal editing features, flexibility within the algorithms used for R-peak detection and HRV quantification, and includes graphical functionality for batch processing. Thereby, providing a platform suited to either physician or researcher. METHODS: RR-APET's software was developed in the Python language and is modular in format, providing a range of different modules for established R-peak detection algorithms, as well as an embedded template for alternate algorithms. These modules also include several easily adjustable features, allowing the user to optimise any of the algorithms for different ECG signals or databases. Additionally, the software's user-friendly GUI platform can be operated by both researchers or medical professionals to accomplish different tasks, such as: the in-depth visual analysis of a single ECG, or the analysis multiple signals in a single iteration using batch processing. RR-APET also supports several popular data formats, including text, HDF5, Matlab, and Waveform Database (WFDB) files. RESULTS: The RR-APET platform presents multiple metrics that quantify the heart rate variability features of an R-to-R interval series, including time-domain, frequency-domain, and nonlinear metrics. When known R-peak annotations are available, positive predictability, sensitivity, detection error rate, and accuracy measures are also provided to assess the validity of the implemented R-peak detection algorithm. RR-APET scored an overall usability rating of 4.16 out of a possible 5, when released on a trial basis for user evaluation. CONCLUSIONS: With its unique ability to both create and operate on large databases, this software provides a strong platform from which to conduct further research in the field of HRV analytics and its correlation to patient healthcare outcomes. This software is available free of charge at https://gitlab.com/MegMcC/rr-apet-hrv-analysis-software and can be operated as an executable file within Windows, Mac and Linux systems.